class_docstr: Variant of vanilla numerical encoder, supports dynamic mean re-scaling
code: def __init__(
      self,
      is_target: bool = False,
      positive_domain: bool = False,
      grouped_by=None,
  ):
      super(TsNumericEncoder, self).__init__(
          is_target=is_target, positive_domain=positive_domain
      )
      # time series normalization params
      self.normalizers = None
      self.group_combinations = None
      self.dependencies = grouped_by
      self.output_size = 1
  
file_path: lightwood/lightwood/encoder/numeric/ts_numeric.py
imports: from typing import Union, List, Dict
  import torch
  import numpy
  import pandas
  from lightwood.encoder.numeric import NumericEncoder
label_desc: Use image node_id 1 to create a new TsNumericEncoder object from inherited base classes: NumericEncoder with example: obj = TsNumericEncoder(is_target, positive_domain, grouped_by)